How to use distinguish method in hypothesis

Best Python code snippet using hypothesis

validate_kabi.py

Source:validate_kabi.py

1import torch2import argparse3import torch.nn as nn4from networks.rps_net_mlp import RPS_net_mlp5import torch.nn.functional as F6import os7from utils.logger import mkdir_if_missing, Logger8from networks.resnet18_for_cifer100 import resnet189from utils.Utils import accuracy, get_val_loader10from utils.AverageMeter import AverageMeter11import sys12from torchvision import models13def get_parser():14    parser = argparse.ArgumentParser()15    parser.add_argument("--data_root", type=str, default='../datasets_folder')16    parser.add_argument("--dataset_name", type=str, default='cifar100')17    parser.add_argument("--batch_size", type=int, default=16)18    parser.add_argument("--model", type=str, default='resnet18')19    parser.add_argument("--model_root", type=str, default='../model')20    parser.add_argument("--gpu_id", type=str, default='0')21    parser.add_argument("--num_of_sample_group", type=int, default=2)22    parser.add_argument("--phases", type=int, default=10)23    return parser24_model_dict = {  # model dict25    'rps_net_mlp':RPS_net_mlp,26    'resnet18': resnet18,27    'standard_resnet18': models.resnet1828}29# compute  probabilities the data contunnum belong to each batch30# for discriminating 'batch' in  mini-batch gradient descent and 'batch' in batch identifier we change the name of batch to task31def predict_task(distinguish_tasks_model, images):32    outputs = distinguish_tasks_model(images)33    outputs = outputs.sum(0)34    return F.softmax(outputs)35def predict_class(distinguish_classes_model, images, task_probability, labels):  # è®¡ç®å±äºæ¯ä¸ªç±»å«çæ¦ç36    opts = get_parser().parse_args()37    num_batch = len(images)//opts.batch_size + 1  # ä¸ç»å¾çåå¤å°ä¸ªbatchè®ç»å®38    outputs=None39    for i in range(num_batch):40        outputs_part = distinguish_classes_model(images[opts.batch_size * i:opts.batch_size*(i+1)])  # åè¡¨æªåæ¶ï¼å³ä¾§ä¸æ å¤§äºåè¡¨é¿åº¦ä¹å¯41        class_num_for_single_task = outputs_part.shape[1]//task_probability.shape[0]  # æ»ç±»å«ä¸ªæ°/ä»»å¡æ°=æ¯ä¸ªä»»å¡åå«çç±»å«ä¸ªæ°42        outputs_part = F.softmax(outputs_part)  # outputç»è¿softmaxæ£åååçç»æ43        for task_id, p in enumerate(task_probability):44            outputs_part[:, task_id * class_num_for_single_task:(task_id + 1) * class_num_for_single_task] = outputs_part[: ,task_id * class_num_for_single_task:(task_id + 1) * class_num_for_single_task] * p45        if i == 0:46            outputs=outputs_part47        else:48            outputs=torch.cat((outputs, outputs_part), dim=0)  # æè¡æ¼æ¥49    return outputs50def predict(val_dataset_path_list, distinguish_tasks_model, distinguish_classes_model, num_of_sample_group, dataset_name):  # é¢æµå¾ççç±»å«51    opts = get_parser().parse_args()52    top5 = AverageMeter()53    top1 = AverageMeter()54    for val_dataset_path in val_dataset_path_list:  # åæ¹æ¬¡éªè¯éªè¯é55        val_loader = get_val_loader(opts.dataset_name, val_dataset_path,batch_size=opts.num_of_sample_group)56        for cur_step, (images, labels) in enumerate(val_loader):57            images = images.to(0, dtype=torch.float32)58            labels = labels.to(0, dtype=torch.long)59            task_probability = predict_task(distinguish_tasks_model, images)60            outputs = predict_class(distinguish_classes_model, images, task_probability,  labels)61            # ilsvrc2012 ââtop5   mnist,cifar100ââtop162            if opts.dataset_name == 'ilsvrc2012':63                _, prec5 = accuracy(outputs, labels, topk=(1,5))64                top5.update(prec5.item(), images.size(0))65            elif opts.dataset_name in ['mnist','cifar100']:66                prec1 = accuracy(outputs,labels)[0]67                top1.update(prec1.item(), images.size(0))68    return top5.avg if opts.dataset_name == 'ilsvrc2012' else top1.avg69def main():  # éªè¯æµç¨70    # è¯»ååæ°71    opts = get_parser().parse_args()72    os.environ['CUDA_VISIBLE_DEVICES'] = opts.gpu_id73    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')74    # dir and log75    mkdir_if_missing('../logs')76    sys.stdout = Logger(os.path.join('../logs', opts.dataset_name,str(opts.phases)+'phases', 'validate_kabi_result.txt'))77    distinguish_task_model_dir = os.path.join(opts.model_root, opts.dataset_name,str(opts.phases)+'phases','batch_identifiers')78    distinguish_task_model_filename_list = os.listdir(distinguish_task_model_dir)79    distinguish_task_model_path_list = []  # ææä¸å®¶æ¨¡åè·¯å¾80    for distinguish_task_model_filename in distinguish_task_model_filename_list:81        distinguish_task_model_path = os.path.join(distinguish_task_model_dir, distinguish_task_model_filename)82        distinguish_task_model_path_list.append(distinguish_task_model_path)83    distinguish_class_model_dir = os.path.join(opts.model_root, opts.dataset_name, str(opts.phases)+'phases','amalgamation_models')84    distinguish_class_model_filename_list = os.listdir(distinguish_class_model_dir)85    distinguish_class_model_path_list = []  # ææèåæ¨¡åè·¯å¾86    for distinguish_class_model_filename in distinguish_class_model_filename_list:87        distinguish_class_model_path = os.path.join(distinguish_class_model_dir, distinguish_class_model_filename)88        distinguish_class_model_path_list.append(distinguish_class_model_path)89    val_dataset_dir = os.path.join(opts.data_root, opts.dataset_name, str(opts.phases)+'phases','separated/test/batch_test')90    val_dataset_filename_list = os.listdir(val_dataset_dir)91    val_dataset_path_list = []  # ææéªè¯æ°æ®éè·¯å¾92    for val_dataset_filename in val_dataset_filename_list:93        val_dataset_path = os.path.join(val_dataset_dir, val_dataset_filename)94        val_dataset_path_list.append(val_dataset_path)95    val_dataset_path_list_for_all_tasks = []  # ç»è£éªè¯é96    for i in range(len(distinguish_class_model_path_list)):97        val_dataset_path_list_for_all_tasks.append(val_dataset_path_list[:i+2])98    task_and_class_model_and_val_dataset_path_list = zip(99                                    distinguish_task_model_path_list,100                                    distinguish_class_model_path_list,101                                    val_dataset_path_list_for_all_tasks)102    for task_and_class_model_path in task_and_class_model_and_val_dataset_path_list:103        model_for_task = _model_dict[opts.model]()104        print('batch identifier pathï¼', task_and_class_model_path[0])105        task_model_state = torch.load(task_and_class_model_path[0], map_location=lambda storage, loc: storage)106        num_batches = task_model_state['num_batches']107        model_for_task.fc = nn.Linear(model_for_task.fc.in_features, num_batches)108        model_for_task.load_state_dict(task_model_state['state_dict'])109        model_for_task.to(device)110        model_for_task.eval()111        model_for_class = _model_dict[opts.model]()112        class_model_state = torch.load(task_and_class_model_path[1], map_location=lambda storage, loc: storage)113        num_classes = class_model_state['num_classes']114        model_for_class.fc = nn.Linear(model_for_class.fc.in_features, num_classes)115        model_for_class.load_state_dict(class_model_state['state_dict'])116        model_for_class.to(device)117        model_for_class.eval()118        print('batch identifier path', task_and_class_model_path[0])119        print('amalgamation model path', task_and_class_model_path[1])120        print('datasets listï¼', task_and_class_model_path[2])121        print('accurcyï¼', predict(task_and_class_model_path[2], model_for_task, model_for_class, opts.num_of_sample_group, opts.dataset_name))122if __name__ == '__main__':...

test_distinguish_between_pn_samples_001.py

Source:test_distinguish_between_pn_samples_001.py

1# Copyright 2019 Huawei Technologies Co., Ltd2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7# http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14"""15################################################16Testcase_PrepareCondition:17Testcase_TestSteps:18Testcase_ExpectedResult:19"""20import os21import pytest22from tests.common.base import TestBase23from tests.common.test_run.ascend.distinguish_between_pn_samples_run import distinguish_between_pn_samples_run24#from print_debug import print_debug25############################################################26# TestCase= class: put to tests/*/27############################################################28class TestCase(TestBase):29    def setup(self):30        case_name = "test_akg_distinguish_between_pn_samples_001"31        case_path = os.getcwd()32        self.params_init(case_name, case_path)33        self.caseresult = True34        self._log.info("============= {0} Setup case============".format(self.casename))35        self.testarg = [36            # testflag,opfuncname,testRunArgs, dimArgs37            ("01_distinguish_between_pn_samples_01", distinguish_between_pn_samples_run, ((8, 4718, 16), 0.618, "float16")),38            ("01_distinguish_between_pn_samples_02", distinguish_between_pn_samples_run, ((8, 4718, 16), 0.618, "float32")),39            ("01_distinguish_between_pn_samples_03", distinguish_between_pn_samples_run, ((8, 4718, 16), 0.222, "float16")),40            ("01_distinguish_between_pn_samples_04", distinguish_between_pn_samples_run, ((8, 4718, 16), 0.222, "float32")),41            ("01_distinguish_between_pn_samples_05", distinguish_between_pn_samples_run, ((8, 4718, 16), 0.999, "float16")),42            ("01_distinguish_between_pn_samples_06", distinguish_between_pn_samples_run, ((8, 4718, 16), 0.999, "float32")),43        ]44        self.testarg_2 = [45            # testflag,opfuncname,testRunArgs, dimArgs46            ("01_distinguish_between_pn_samples_01", distinguish_between_pn_samples_run, ((8, 8732, 16), 0.618, "float16")),47            ("01_distinguish_between_pn_samples_02", distinguish_between_pn_samples_run, ((8, 8732, 16), 0.618, "float32")),48            ("01_distinguish_between_pn_samples_03", distinguish_between_pn_samples_run, ((8, 8732, 16), 0.222, "float16")),49            ("01_distinguish_between_pn_samples_04", distinguish_between_pn_samples_run, ((8, 8732, 16), 0.222, "float32")),50            ("01_distinguish_between_pn_samples_05", distinguish_between_pn_samples_run, ((8, 8732, 16), 0.999, "float16")),51            ("01_distinguish_between_pn_samples_06", distinguish_between_pn_samples_run, ((8, 8732, 16), 0.999, "float32")),52        ]53        self.testarg_aic_cloud = [54            # testflag,opfuncname,testRunArgs, dimArgs55            ("01_distinguish_between_pn_samples_01", distinguish_between_pn_samples_run, ((8, 18, 16), 0.618, "float16")),56            ("01_distinguish_between_pn_samples_02", distinguish_between_pn_samples_run, ((8, 18, 16), 0.618, "float32")),57        ]58        return59    @pytest.mark.level060    @pytest.mark.platform_arm_ascend_training61    @pytest.mark.platform_x86_ascend_training62    @pytest.mark.env_onecard63    def test_run(self):64        self.common_run(self.testarg)65    def test_run_2(self):66        self.common_run(self.testarg)67    def test_run_ci(self):68        self.common_run(self.testarg_aic_cloud)69    def teardown(self):70        self._log.info("============= {0} Teardown============".format(self.casename))71        return72if __name__ == "__main__":73    t = TestCase()74    t.setup()75    t.test_run()...

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